Noise contributions in an inducible genetic switch: a whole-cell simulation study

Area of Science:

• * Molecular Biology
• * Systems Biology
• * Biophysics

Background:

• * Stochastic gene expression leads to cell-to-cell variability.
• * The lac genetic switch in Escherichia coli is a model system for studying gene expression dynamics.
• * Understanding gene expression noise is crucial for comprehending bacterial adaptation.

Purpose of the Study:

• * To analyze and compare the behavior of the inducible lac genetic switch using well-stirred and spatially resolved simulations.
• * To incorporate a new kinetic model with parameters from single-molecule fluorescence experiments and in vitro rate constants.
• * To investigate the impact of cell structure, growth conditions, and cytoplasmic crowding on gene expression noise.

Main Methods:

• * Developed a new kinetic model for the lac operon switching.
• * Performed well-stirred and spatially resolved simulations of Escherichia coli.
• * Utilized cryoelectron tomography and proteomics data to construct in vivo spatial models.
• * Applied maximum likelihood estimation to extract stochastic rates from simulation data.

Main Results:

• * In well-stirred systems, noise in the lac circuit peaks near the switching threshold.
• * Spatial simulations reveal that cell structure and crowding influence noise contributions and repressor rebinding.
• * Fast-growth cells show slightly decreased noise and increased repressor rebinding due to anomalous subdiffusion.
• * Slow-growth cells exhibit increased mRNA localization and internal inducer concentration, altering repressor-operator complex dynamics and transcriptional bursting.

Conclusions:

• * The analytic two-state model of gene expression can accurately extract stochastic rates from simulation data.
• * Cell structure and growth conditions significantly modulate gene expression noise and regulatory dynamics in E. coli.
• * Spatial heterogeneity plays a critical role in bacterial cell behavior and adaptation.